---
language: en
title: "Schema.org"
description: "Schema.org is a vocabulary of types and properties for describing content. Learn how it relates to JSON-LD, structured data, and validation, without promises of citations."
canonical: "https://tropk.ai/en/resources/glossary/schema-org"
last_modified: "2026-10-12T16:14:57.292Z"
---

# Schema.org

Reviewed on 2026-10-06.

## What it means

Schema.org is a shared vocabulary of types and properties for describing things and their relationships on the web. It includes types such as Organization, Person, Article, and Product. The vocabulary lets you represent the meaning of data; it can be used in different formats, such as [JSON-LD](/en/resources/glossary/json-ld) and Microdata. It is neither a plugin nor a search engine.

## Why it matters for marketing

For marketing, the main consideration is choosing descriptions that accurately reflect the page and the offer. A team can represent authorship, organization, and products consistently across content. This makes maintenance and review easier. When pursuing a search feature, however, you need to consult the documentation of the platform that consumes this data: being part of the vocabulary does not mean every platform uses it.

## Example in practice

An article uses Article to provide its title, author, and publication details, and links its publisher to an Organization. The organization should correspond to the real company. An internal testimonial, however, should not be turned into an independent review just because a Review type is available. The schema must preserve the meaning of the content, including its limitations and origin.

## How to apply or verify it

Consult the definition of the type and its properties on Schema.org. Then check the rules for the desired feature on the target platform. Validate the document and compare each piece of information with what the page presents. Keep identifiers consistent and avoid generating irrelevant types on every URL just because a template makes this possible.

## Important distinctions

Schema.org describes meaning; JSON-LD describes how the representation is written. A validator can confirm structure without confirming the truth of the facts. Markup also does not guarantee a rich result, a better position, or an [AI recommendation](/en/resources/glossary/ai-recommendation). The benefit should be explained in terms of clarity, consistency, and documented eligibility, without inventing a universal ranking effect.

## Sources and references

- [Getting started with schema.org](<https://schema.org/docs/gs.html>) — Schema.org
- [Introduction to structured data markup in Google Search](<https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data>) — Google Search Central

## Continue a leitura

- [Structured data](</en/resources/glossary/structured-data>)
- [Entity](</en/resources/glossary/entity>)
- [JSON-LD](</en/resources/glossary/json-ld>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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